Chapter 6 Diversity analysis

6.1 Alpha diversity

# Calculate Hill numbers
richness <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 0) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(richness = 1) %>%
  rownames_to_column(var = "sample")

neutral <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(neutral = 1) %>%
  rownames_to_column(var = "sample")

phylogenetic <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, tree = genome_tree) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(phylogenetic = 1) %>%
  rownames_to_column(var = "sample")

# Aggregate basal GIFT into elements
dist <- genome_gifts %>%
  to.elements(., GIFT_db3) %>%
  traits2dist(., method = "gower")

functional <- genome_counts_filt %>%
  column_to_rownames(var = "genome") %>%
  dplyr::select(where(~ !all(. == 0))) %>%
  hilldiv(., q = 1, dist = dist) %>%
  t() %>%
  as.data.frame() %>%
  dplyr::rename(functional = 1) %>%
  rownames_to_column(var = "sample") %>%
  mutate(functional = if_else(is.nan(functional), 1, functional))

# Merge all metrics
alpha_div <- richness %>%
  full_join(neutral, by = join_by(sample == sample)) %>%
  full_join(phylogenetic, by = join_by(sample == sample)) %>%
  full_join(functional, by = join_by(sample == sample))

6.1.1 Wild samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="0_Wild") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.2 Acclimation samples

alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="1_Acclimation") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = Population, group=Population, color=Population, fill=Population)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#008080', "#d57d2c")) +
      scale_fill_manual(name="Population",
          breaks=c("Cold_wet","Hot_dry"),
          labels=c("Cold","Hot"),
          values=c('#00808050', "#d57d2c50")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.58) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

6.1.3 Post-Transplant_1 samples

bxp1<-alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="5_Post-FMT1") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-Cold","Hot-Hot", "Cold-Hot"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
        scale_x_discrete(labels = c("Control" = "Cold-Cold", "Hot_control" = "Hot-Hot", "Treatment" = "Cold-Hot")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")


# Add significance bars with p-values
bxp1 + 
  geom_signif(comparisons = list(c("Control", "Hot_control"), c("Control", "Treatment"), c("Hot_control", "Treatment")),
              map_signif_level = TRUE)

6.1.4 Post-Transplant_2 samples

bxp1<-alpha_div %>%
  pivot_longer(-sample, names_to = "metric", values_to = "value") %>%
  left_join(., sample_metadata, by = join_by(sample == Tube_code)) %>%
  filter(time_point=="6_Post-FMT2") %>%
  mutate(metric=factor(metric,levels=c("richness","neutral","phylogenetic","functional"))) %>%
      ggplot(aes(y = value, x = type, group=type, color=type, fill=type)) +
      geom_boxplot(outlier.shape = NA) +
      geom_jitter(alpha=0.5) +
      scale_color_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA","#d57d2c", "#76b183")) +
      scale_fill_manual(name="Type",
          breaks=c("Control","Hot_control", "Treatment"),
          labels=c("Cold-control","Warm-control", "Cold-intervention"),
          values=c("#4477AA50","#d57d2c50","#76b18350")) +
          scale_x_discrete(labels = c("Control" = "Cold-Cold", "Hot_control" = "Hot-Hot", "Treatment" = "Cold-Hot")) +
      facet_wrap(. ~ metric,scales = "free") +
      coord_cartesian(xlim = c(1, NA)) +
      stat_compare_means(size=3, label.x=.7) +
      theme_classic() +
        theme(
    strip.background = element_blank(),
    panel.grid.minor.x = element_line(size = .1, color = "grey"),
    axis.title.x = element_blank(),
    axis.title.y = element_text(size=10),
    axis.text.x = element_text(angle = 45, hjust = 1),
    # Increase plot size
    plot.title = element_text(size = 10),
    axis.text = element_text(size = 8),
    axis.title = element_text(size = 8)
      ) +
  ylab("Alpha diversity")

# Add significance bars with p-values
bxp1 + 
  geom_signif(comparisons = list(c("Control", "Hot_control"), c("Control", "Treatment"), c("Hot_control", "Treatment")),
              map_signif_level = TRUE)

6.2 Beta diversity

beta_q0n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 0)

beta_q1n <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1)

beta_q1p <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, tree = genome_tree)

beta_q1f <- genome_counts_filt %>%
  column_to_rownames(., "genome") %>%
  hillpair(., q = 1, dist = dist)

6.3 Permanovas

6.3.0.1 Load required data

6.3.1 1. Are the wild populations similar?

6.3.1.1 Wild: cold vs warm adapted lizards

6.3.1.1.1 Number of samples used
[1] 27

6.3.1.2 Richness

richness <- as.matrix(beta_q0n$S)
richness <- as.dist(richness[rownames(richness) %in% samples_to_keep,
               colnames(richness) %in% samples_to_keep])
betadisper(richness, subset_meta$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000012 0.000012 0.0012    999  0.978
Residuals 25 0.257281 0.010291                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet             0.98
Hot_dry   0.97302        
adonis2(richness ~ Population,
        data = subset_meta %>% arrange(match(Tube_code,labels(richness))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 1.542719 0.2095041 6.625717 0.001
Residual 25 5.820951 0.7904959 NA NA
Total 26 7.363669 1.0000000 NA NA

6.3.1.3 Neutral

neutral <- as.matrix(beta_q1n$S)
neutral <- as.dist(neutral[rownames(neutral) %in% samples_to_keep,
               colnames(neutral) %in% samples_to_keep])
betadisper(neutral, subset_meta$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.000048 0.0000476 0.0044    999  0.951
Residuals 25 0.270114 0.0108046                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.954
Hot_dry   0.94763        
adonis2(neutral ~ Population,
        data = subset_meta %>% arrange(match(Tube_code,labels(neutral))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 1.918266 0.2608511 8.822682 0.001
Residual 25 5.435610 0.7391489 NA NA
Total 26 7.353876 1.0000000 NA NA

6.3.1.4 Phylogenetic

phylo <- as.matrix(beta_q1p$S)
phylo <- as.dist(phylo[rownames(phylo) %in% samples_to_keep,
               colnames(phylo) %in% samples_to_keep])
betadisper(phylo, subset_meta$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.03585 0.035847 2.4912    999  0.133
Residuals 25 0.35973 0.014389                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.126
Hot_dry   0.12705        
adonis2(phylo ~ Population,
        data = subset_meta %>% arrange(match(Tube_code,labels(phylo))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 0.3218613 0.2162815 6.899207 0.001
Residual 25 1.1662981 0.7837185 NA NA
Total 26 1.4881594 1.0000000 NA NA

6.3.1.5 Functional

func <- as.matrix(beta_q1f$S)
func <- as.dist(func[rownames(func) %in% samples_to_keep,
               colnames(func) %in% samples_to_keep])
betadisper(func, subset_meta$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq     F N.Perm Pr(>F)
Groups     1 0.019387 0.019387 1.653    999  0.199
Residuals 25 0.293200 0.011728                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.205
Hot_dry   0.21033        
adonis2(func ~ Population,
        data = subset_meta %>% arrange(match(Tube_code,labels(func))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 0.0831048 0.1680538 5.05002 0.067
Residual 25 0.4114083 0.8319462 NA NA
Total 26 0.4945131 1.0000000 NA NA
beta_q0n_nmds_wild <- richness %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta, by = join_by(sample == Tube_code))

beta_q1n_nmds_wild <- neutral %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta, by = join_by(sample == Tube_code))

beta_q1p_nmds_wild <- phylo %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta, by = join_by(sample == Tube_code))

beta_q1f_nmds_wild <- func %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                vegan::scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta, by = join_by(sample == Tube_code))

6.3.2 2. Effect of acclimation

6.3.2.0.1 Number of samples used
[1] 26

6.3.2.1 Richness

richness_accli <- as.matrix(beta_q0n$S)
richness_accli <- as.dist(richness_accli[rownames(richness_accli) %in% samples_to_keep_accli,
               colnames(richness_accli) %in% samples_to_keep_accli])
betadisper(richness_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.093187 0.093187 11.812    999  0.002 **
Residuals 24 0.189340 0.007889                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.002
Hot_dry  0.0021532        
adonis2(richness_accli ~ Population,
        data = subset_meta_accli %>% arrange(match(Tube_code,labels(richness_accli))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 1.639630 0.1929088 5.736415 0.001
Residual 24 6.859879 0.8070912 NA NA
Total 25 8.499509 1.0000000 NA NA

6.3.2.2 Neutral

neutral_accli <- as.matrix(beta_q1n$S)
neutral_accli <- as.dist(neutral_accli[rownames(neutral_accli) %in% samples_to_keep_accli,
               colnames(neutral_accli) %in% samples_to_keep_accli])
betadisper(neutral_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.05603 0.056026 4.1918    999  0.049 *
Residuals 24 0.32077 0.013365                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.051
Hot_dry  0.051717        
adonis2(neutral_accli ~ Population,
        data = subset_meta_accli %>% arrange(match(Tube_code,labels(neutral_accli))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 1.961192 0.2493171 7.970889 0.001
Residual 24 5.905063 0.7506829 NA NA
Total 25 7.866255 1.0000000 NA NA

6.3.2.3 Phylogenetic

phylo_accli <- as.matrix(beta_q1p$S)
phylo_accli <- as.dist(phylo_accli[rownames(phylo_accli) %in% samples_to_keep_accli,
               colnames(phylo_accli) %in% samples_to_keep_accli])
betadisper(phylo_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.03637 0.036365 2.3087    999  0.136
Residuals 24 0.37804 0.015752                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.133
Hot_dry   0.14172        
adonis2(phylo_accli ~ Population,
        data = subset_meta_accli %>% arrange(match(Tube_code,labels(phylo_accli))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 0.2113395 0.1379515 3.84066 0.007
Residual 24 1.3206449 0.8620485 NA NA
Total 25 1.5319844 1.0000000 NA NA

6.3.2.4 Functional

func_accli <- as.matrix(beta_q1f$S)
func_accli <- as.dist(func_accli[rownames(func_accli) %in% samples_to_keep_accli,
               colnames(func_accli) %in% samples_to_keep_accli])
betadisper(func_accli, subset_meta_accli$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00409 0.004087 0.1789    999  0.673
Residuals 24 0.54821 0.022842                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet             0.68
Hot_dry   0.67607        
adonis2(func_accli ~ Population,
        data = subset_meta_accli %>% arrange(match(Tube_code,labels(func_accli))),
        permutations = 999) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 1 0.00401769 0.005973179 0.1442177 0.636
Residual 24 0.66860401 0.994026821 NA NA
Total 25 0.67262170 1.000000000 NA NA
beta_q0n_nmds_accli <- richness_accli %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_accli, by = join_by(sample == Tube_code))

beta_q1n_nmds_accli <- neutral_accli %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE, trace=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_accli, by = join_by(sample == Tube_code))

beta_q1p_nmds_accli <- phylo_accli %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_accli, by = join_by(sample == Tube_code))

beta_q1f_nmds_accli <- func_accli %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  vegan::scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_accli, by = join_by(sample == Tube_code))

6.3.3 3. Comparison between Wild and Acclimation

6.3.3.0.1 Number of samples used
[1] 53
6.3.3.0.2 Richness
richness_accli1 <- as.matrix(beta_q0n$S)
richness_accli1 <- as.dist(richness_accli1[rownames(richness_accli1) %in% samples_to_keep_accli1,
               colnames(richness_accli1) %in% samples_to_keep_accli1])
betadisper(richness_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)  
Groups     1 0.03286 0.032865 2.9698    999  0.097 .
Residuals 51 0.56438 0.011066                       
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.089
Hot_dry   0.09089        
adonis2(richness_accli1 ~ Population*time_point,
        data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(richness_accli1))),
        permutations = 999,
        strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(richness_accli1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 3.762933 0.2288365 4.846784 0.001
Residual 49 12.680830 0.7711635 NA NA
Total 52 16.443763 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_accli1_arrange <- column_to_rownames(subset_meta_accli1, "Tube_code")
subset_meta_accli1_arrange<-subset_meta_accli1_arrange[labels(richness_accli1),]

pairwise<-pairwise.adonis(richness_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 1.6396299 5.736415 0.19290877 0.001 0.006 *
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.5273732 1.906862 0.05462715 0.008 0.048 .
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 1.5558412 5.190707 0.17782052 0.001 0.006 *
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 1.8259388 8.319131 0.24968031 0.001 0.006 *
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.3856333 1.736034 0.09788177 0.008 0.048 .
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 1.5427188 6.625717 0.20950408 0.001 0.006 *
6.3.3.0.3 Neutral
neutral_accli1 <- as.matrix(beta_q1n$S)
neutral_accli1 <- as.dist(neutral_accli1[rownames(neutral_accli1) %in% samples_to_keep_accli1,
               colnames(neutral_accli1) %in% samples_to_keep_accli1])
betadisper(neutral_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01431 0.014310 1.1177    999  0.324
Residuals 51 0.65296 0.012803                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.312
Hot_dry   0.29539        
adonis2(neutral_accli1 ~ Population*time_point,
        data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(neutral_accli1))),
        permutations = 999,
        strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(neutral_accli1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 4.735485 0.2945657 6.820252 0.001
Residual 49 11.340673 0.7054343 NA NA
Total 52 16.076158 1.0000000 NA NA
pairwise<-pairwise.adonis(neutral_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 1.9611917 7.970889 0.24931708 0.001 0.006 *
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.7466371 3.064914 0.08498327 0.001 0.006 *
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 2.1421077 8.176866 0.25412251 0.001 0.006 *
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 2.0371666 10.078295 0.28730857 0.001 0.006 *
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.6314393 3.060027 0.16054681 0.001 0.006 *
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 1.9182663 8.822682 0.26085105 0.001 0.006 *
6.3.3.0.4 Phylogenetic
phylo_accli1 <- as.matrix(beta_q1p$S)
phylo_accli1 <- as.dist(phylo_accli1[rownames(phylo_accli1) %in% samples_to_keep_accli1,
               colnames(phylo_accli1) %in% samples_to_keep_accli1])
betadisper(phylo_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq     F N.Perm Pr(>F)
Groups     1 0.00001 0.0000111 6e-04    999  0.981
Residuals 51 0.89017 0.0174543                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.982
Hot_dry   0.97998        
adonis2(phylo_accli1 ~ Population*time_point,
        data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(phylo_accli1))),
        permutations = 999,
        strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(phylo_accli1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.7622566 0.2345983 5.006223 0.001
Residual 49 2.4869430 0.7654017 NA NA
Total 52 3.2491997 1.0000000 NA NA
pairwise<-pairwise.adonis(phylo_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 0.2113395 3.840660 0.1379515 0.011 0.066
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.2076830 4.216762 0.1133028 0.002 0.012 .
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 0.3635093 4.799786 0.1666605 0.001 0.006 *
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 0.2121479 7.924059 0.2406769 0.002 0.012 .
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.2092433 3.885515 0.1953942 0.001 0.006 *
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 0.3218613 6.899207 0.2162815 0.001 0.006 *
6.3.3.0.5 Functional
func_accli1 <- as.matrix(beta_q1f$S)
func_accli1 <- as.dist(func_accli1[rownames(func_accli1) %in% samples_to_keep_accli1,
               colnames(func_accli1) %in% samples_to_keep_accli1])
betadisper(func_accli1, subset_meta_accli1$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.01374 0.013738 0.7923    999  0.399
Residuals 51 0.88435 0.017340                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet              0.4
Hot_dry    0.3776        
adonis2(func_accli1 ~ Population*time_point,
        data = subset_meta_accli1 %>% arrange(match(Tube_code,labels(func_accli1))),
        permutations = 999,
        strata = subset_meta_accli1 %>% arrange(match(Tube_code,labels(func_accli1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.1024301 0.08662587 1.54908 0.194
Residual 49 1.0800123 0.91337413 NA NA
Total 52 1.1824424 1.00000000 NA NA
pairwise<-pairwise.adonis(func_accli1,subset_meta_accli1_arrange$Population_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Cold_wet.1_Acclimation vs Hot_dry.1_Acclimation 1 0.0040176901 0.144217742 0.0059731793 0.634 1.000
Cold_wet.1_Acclimation vs Cold_wet.0_Wild 1 0.0002775823 0.009969841 0.0003020251 0.704 1.000
Cold_wet.1_Acclimation vs Hot_dry.0_Wild 1 0.0871206378 3.762920746 0.1355376396 0.079 0.474
Hot_dry.1_Acclimation vs Cold_wet.0_Wild 1 0.0025235151 0.120315257 0.0047895600 0.606 1.000
Hot_dry.1_Acclimation vs Hot_dry.0_Wild 1 0.0409732757 4.066330477 0.2026444487 0.092 0.552
Cold_wet.0_Wild vs Hot_dry.0_Wild 1 0.0831047968 5.050019518 0.1680537849 0.060 0.360
beta_richness_nmds_accli1 <- richness_accli1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_accli1, by = c("sample" = "Tube_code"))

beta_neutral_nmds_accli1 <- neutral_accli1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_accli1, by = c("sample" = "Tube_code"))

beta_phylo_nmds_accli1 <- phylo_accli1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_accli1, by = join_by(sample == Tube_code))

beta_func_nmds_accli1 <- func_accli1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_accli1, by = join_by(sample == Tube_code))

6.3.4 4. Effect of FMT on microbiota community

6.3.4.1 Comparison between Acclimation vs Post-FMT1

6.3.4.1.1 Number of samples used
[1] 52
6.3.4.1.2 Richness
richness_post3 <- as.matrix(beta_q0n$S)
richness_post3 <- as.dist(richness_post3[rownames(richness_post3) %in% samples_to_keep_post3,
               colnames(richness_post3) %in% samples_to_keep_post3])
betadisper(richness_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.10843 0.108427 25.578    999  0.001 ***
Residuals 50 0.21195 0.004239                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
           Cold_wet Hot_dry
Cold_wet              0.001
Hot_dry  6.0928e-06        
adonis2(richness_post3 ~ Population*time_point,
        data = subset_meta_post3 %>% arrange(match(Tube_code,labels(richness_post3))),
        permutations = 999,
        strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(richness_post3))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 3.472191 0.193401 3.836375 0.001
Residual 48 14.481131 0.806599 NA NA
Total 51 17.953321 1.000000 NA NA
#Arrange of metadata dataframe
subset_meta_post3_arrange <- column_to_rownames(subset_meta_post3, "Tube_code")
subset_meta_post3_arrange<-subset_meta_post3_arrange[labels(richness_post3),]

pairwise <- pairwise.adonis(richness_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.3657243 1.123239 0.06966584 0.235 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.2605860 4.943717 0.24788342 0.001 0.015 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.6804988 2.100611 0.12283837 0.002 0.030 .
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8481258 2.673290 0.16033371 0.001 0.015 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.1180661 3.809275 0.20252111 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.3606630 5.087152 0.24124415 0.001 0.015 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.7216200 2.172734 0.11956009 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9551308 2.926054 0.16322910 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.2263345 4.039487 0.20157637 0.001 0.015 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.4319792 5.384836 0.25180628 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8172413 3.194690 0.17558364 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.5796135 2.441615 0.13239702 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.5615418 1.729004 0.10335366 0.014 0.210
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.8438429 2.793772 0.14865413 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.3734921 1.268929 0.07799710 0.100 1.000
6.3.4.1.3 Neutral
neutral_post3 <- as.matrix(beta_q1n$S)
neutral_post3 <- as.dist(neutral_post3[rownames(neutral_post3) %in% samples_to_keep_post3,
               colnames(neutral_post3) %in% samples_to_keep_post3])
betadisper(neutral_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.09089 0.090889 12.898    999  0.002 **
Residuals 50 0.35233 0.007047                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
           Cold_wet Hot_dry
Cold_wet              0.001
Hot_dry  0.00074926        
adonis2(neutral_post3 ~ Population*time_point,
        data = subset_meta_post3 %>% arrange(match(Tube_code,labels(neutral_post3))),
        permutations = 999,
        strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(neutral_post3))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 4.443476 0.2628643 5.705637 0.001
Residual 48 12.460591 0.7371357 NA NA
Total 51 16.904067 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.2759858 0.9928976 0.06208366 0.457 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.4146464 6.5078325 0.30257965 0.001 0.015 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.8153894 3.0970603 0.17113610 0.001 0.015 .
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 1.1809241 4.4856470 0.24265567 0.001 0.015 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.4321524 5.7774260 0.27806264 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.6347704 6.8326887 0.29925029 0.001 0.015 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.9517634 3.3715700 0.17404733 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 1.3127773 4.6298256 0.23585668 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.6713369 6.2395460 0.28056085 0.001 0.015 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.5409781 6.8338056 0.29928456 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9133614 4.0964534 0.21451383 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.6954835 3.2951234 0.17077493 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.6051778 2.2508491 0.13047758 0.016 0.240
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 1.0528902 4.1436369 0.20570451 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.4150076 1.6372683 0.09840968 0.058 0.870
6.3.4.1.4 Phylogenetic
phylo_post3 <- as.matrix(beta_q1p$S)
phylo_post3 <- as.dist(phylo_post3[rownames(phylo_post3) %in% samples_to_keep_post3,
               colnames(phylo_post3) %in% samples_to_keep_post3])
betadisper(phylo_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.04088 0.040882 2.9254    999  0.102
Residuals 50 0.69874 0.013975                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.093
Hot_dry  0.093394        
adonis2(phylo_post3 ~ Population*time_point,
        data = subset_meta_post3 %>% arrange(match(Tube_code,labels(phylo_post3))),
        permutations = 999,
        strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(phylo_post3))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.6468577 0.2180259 4.461035 0.004
Residual 48 2.3200272 0.7819741 NA NA
Total 51 2.9668849 1.0000000 NA NA
pairwise <- pairwise.adonis(phylo_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.05690611 0.7893300 0.04999136 0.559 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.10866997 3.0547314 0.16919285 0.010 0.150
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.11760287 2.9988032 0.16661126 0.018 0.270
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.09624422 1.7247115 0.10968160 0.134 1.000
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.18586788 4.1904227 0.21836011 0.004 0.060
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.23108846 4.0521838 0.20208192 0.002 0.030 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.26358465 4.3608960 0.21417997 0.005 0.075
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.25319427 3.2738422 0.17915456 0.045 0.675
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.39050120 5.9837393 0.27218933 0.001 0.015 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 0.14203376 5.4200212 0.25303529 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.09666753 2.3682173 0.13635351 0.020 0.300
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.09252600 2.9824958 0.15711821 0.006 0.090
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.01842535 0.4144162 0.02688498 0.774 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.05987967 1.7387847 0.09802164 0.095 1.000
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.03212966 0.6477782 0.04139746 0.708 1.000
6.3.4.1.5 Functional
func_post3 <- as.matrix(beta_q1f$S)
func_post3 <- as.dist(func_post3[rownames(func_post3) %in% samples_to_keep_post3,
               colnames(func_post3) %in% samples_to_keep_post3])
betadisper(func_post3, subset_meta_post3$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00116 0.0011552 0.0615    999  0.798
Residuals 50 0.93946 0.0187892                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.791
Hot_dry   0.80518        
adonis2(func_post3 ~ Population*time_point,
        data = subset_meta_post3 %>% arrange(match(Tube_code,labels(func_post3))),
        permutations = 999,
        strata = subset_meta_post3 %>% arrange(match(Tube_code,labels(func_post3))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.02861503 0.01961684 0.3201497 0.424
Residual 48 1.43008228 0.98038316 NA NA
Total 51 1.45869731 1.00000000 NA NA
pairwise <- pairwise.adonis(func_post3, subset_meta_post3_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.022076664 0.65002192 0.041534889 0.400 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.016749605 0.52091146 0.033561912 0.475 1.000
Control.1_Acclimation vs Control.5_Post-FMT1 1 -0.008325555 -0.22800110 -0.015434681 0.902 1.000
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.126527918 3.45550519 0.197960767 0.065 0.975
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.051008429 1.23951838 0.076327287 0.316 1.000
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.001022590 0.05430389 0.003382513 0.639 1.000
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.002157067 0.09411569 0.005847832 0.619 1.000
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.056602363 2.56037069 0.145803909 0.148 1.000
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.009569124 0.35095521 0.021463896 0.506 1.000
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 -0.001745663 -0.08225018 -0.005167199 0.703 1.000
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.057758674 2.84545622 0.159449901 0.157 1.000
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.005575266 0.21803560 0.013444020 0.557 1.000
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.119540855 4.84764704 0.244242909 0.076 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.052587837 1.77308932 0.099762584 0.212 1.000
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.012980354 0.44307662 0.028690955 0.464 1.000
beta_richness_nmds_post3 <- richness_post3 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post3, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post3 <- neutral_post3 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post3, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post3 <- phylo_post3 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post3, by = join_by(sample == Tube_code))

beta_func_nmds_post3 <- func_post3 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post3, by = join_by(sample == Tube_code))

6.3.4.2 Comparison between Acclimation vs Post-FMT2

6.3.4.2.1 Number of samples used
[1] 53
6.3.4.2.2 Richness
richness_post4 <- as.matrix(beta_q0n$S)
richness_post4 <- as.dist(richness_post4[rownames(richness_post4) %in% samples_to_keep_post4,
               colnames(richness_post4) %in% samples_to_keep_post4])
betadisper(richness_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.07832 0.078322 11.371    999  0.001 ***
Residuals 51 0.35129 0.006888                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.001
Hot_dry  0.0014303        
adonis2(richness_post4 ~ Population*time_point,
        data = subset_meta_post4 %>% arrange(match(Tube_code,labels(richness_post4))),
        permutations = 999,
        strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(richness_post4))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 3.279021 0.1937289 3.924535 0.001
Residual 49 13.646799 0.8062711 NA NA
Total 52 16.925820 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_post4_arrange <- column_to_rownames(subset_meta_post4, "Tube_code")
subset_meta_post4_arrange<-subset_meta_post4_arrange[labels(richness_post4),]

pairwise <- pairwise.adonis(richness_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.3657243 1.123239 0.06966584 0.246 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.2605860 4.943717 0.24788342 0.001 0.015 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.8072604 2.940901 0.16392161 0.001 0.015 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.4817387 1.660775 0.09968176 0.028 0.420
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.1179704 3.885459 0.20573812 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.3606630 5.087152 0.24124415 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.9130048 3.195028 0.16645080 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.5959230 1.984036 0.11032208 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.2747787 4.275366 0.21086503 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6397330 2.913695 0.15405213 0.001 0.015 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.4575447 6.224524 0.28007456 0.002 0.030 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3276169 1.412318 0.08111028 0.037 0.555
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.6463814 2.560441 0.13795154 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.4796256 1.916520 0.10696943 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.1305044 4.268317 0.21059061 0.001 0.015 .
6.3.4.2.3 Neutral
neutral_post4 <- as.matrix(beta_q1n$S)
neutral_post4 <- as.dist(neutral_post4[rownames(neutral_post4) %in% samples_to_keep_post4,
               colnames(neutral_post4) %in% samples_to_keep_post4])
betadisper(neutral_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.07811 0.078108 9.6342    999  0.001 ***
Residuals 51 0.41347 0.008107                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.002
Hot_dry  0.0031133        
adonis2(neutral_post4 ~ Population*time_point,
        data = subset_meta_post4 %>% arrange(match(Tube_code,labels(neutral_post4))),
        permutations = 999,
        strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(neutral_post4))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 3.823089 0.2357666 5.038847 0.001
Residual 49 12.392477 0.7642334 NA NA
Total 52 16.215567 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.2759858 0.9928976 0.06208366 0.453 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.4146464 6.5078325 0.30257965 0.001 0.015 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 1.1524353 4.9536068 0.24825621 0.001 0.015 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.4748999 1.9609749 0.11561688 0.002 0.030 .
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.3503168 5.4081420 0.26499923 0.001 0.015 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.6347704 6.8326887 0.29925029 0.001 0.015 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 1.3540292 5.3398081 0.25022756 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.6311089 2.4041625 0.13063146 0.002 0.030 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.6125755 5.9825981 0.27215155 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6202327 3.1519868 0.16457754 0.001 0.015 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.5701179 7.6327037 0.32297209 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3634438 1.7083388 0.09647087 0.035 0.525
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 1.0227481 4.6483346 0.22511910 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.5010202 2.2065321 0.12119453 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.3619424 5.7710313 0.26507845 0.001 0.015 .
6.3.4.2.4 Phylogenetic
phylo_post4 <- as.matrix(beta_q1p$S)
phylo_post4 <- as.dist(phylo_post4[rownames(phylo_post4) %in% samples_to_keep_post4,
               colnames(phylo_post4) %in% samples_to_keep_post4])
betadisper(phylo_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq     F N.Perm Pr(>F)  
Groups     1 0.03098 0.030984 2.885    999  0.089 .
Residuals 51 0.54772 0.010740                      
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet             0.08
Hot_dry    0.0955        
adonis2(phylo_post4 ~ Population*time_point,
        data = subset_meta_post4 %>% arrange(match(Tube_code,labels(phylo_post4))),
        permutations = 999,
        strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(phylo_post4))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.6466392 0.2442162 5.277784 0.001
Residual 49 2.0011759 0.7557838 NA NA
Total 52 2.6478151 1.0000000 NA NA
pairwise <- pairwise.adonis(phylo_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.05690611 0.789330 0.04999136 0.542 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.10866997 3.054731 0.16919285 0.010 0.150
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.15591805 4.379209 0.22597458 0.006 0.090
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.07099742 1.879364 0.11134092 0.111 1.000
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.18682367 4.878754 0.24542555 0.002 0.030 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.23108846 4.052184 0.20208192 0.007 0.105
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.36496892 6.396667 0.28560797 0.001 0.015 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.22628210 3.829222 0.19311005 0.024 0.360
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.34830814 5.846334 0.26761166 0.001 0.015 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.10002871 4.383624 0.21505615 0.001 0.015 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 0.12577510 5.060129 0.24027055 0.001 0.015 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.06334378 2.499774 0.13512455 0.022 0.330
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.05927454 2.382025 0.12958449 0.021 0.315
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.06906280 2.722460 0.14541146 0.005 0.075
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.11081709 4.043656 0.20174244 0.001 0.015 .
6.3.4.2.5 Functional
func_post4 <- as.matrix(beta_q1f$S)
func_post4 <- as.dist(func_post4[rownames(func_post4) %in% samples_to_keep_post4,
               colnames(func_post4) %in% samples_to_keep_post4])
betadisper(func_post4, subset_meta_post4$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq     F N.Perm Pr(>F)
Groups     1 0.00006 0.0000601 0.003    999  0.966
Residuals 51 1.03808 0.0203544                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.965
Hot_dry   0.95688        
adonis2(func_post4 ~ Population*time_point,
        data = subset_meta_post4 %>% arrange(match(Tube_code,labels(func_post4))),
        permutations = 999,
        strata = subset_meta_post4 %>% arrange(match(Tube_code,labels(func_post4))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 3 0.02585374 0.01884827 0.313769 0.537
Residual 49 1.34582345 0.98115173 NA NA
Total 52 1.37167719 1.00000000 NA NA
pairwise <- pairwise.adonis(func_post4, subset_meta_post4_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.0220766636 0.650021923 0.0415348890 0.439 1
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.0167496054 0.520911459 0.0335619117 0.457 1
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.0376809680 1.119650309 0.0694587220 0.298 1
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.0292200956 0.920511083 0.0578191917 0.386 1
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0133433458 0.270473784 0.0177122064 0.496 1
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.0010225901 0.054303886 0.0033825127 0.676 1
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 -0.0005177706 -0.025585400 -0.0016016487 0.724 1
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.0013301207 0.072110871 0.0044867082 0.610 1
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0060959077 0.174487757 0.0107878382 0.576 1
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.0010345754 0.055797964 0.0034752533 0.629 1
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 -0.0001056284 -0.006306177 -0.0003942915 0.706 1
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0017235602 0.051851181 0.0032302306 0.773 1
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 -0.0080428882 -0.442986255 -0.0284750185 0.848 1
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 -0.0011796256 -0.034047378 -0.0021324990 0.889 1
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.0036300838 0.110487573 0.0068581148 0.706 1
beta_richness_nmds_post4 <- richness_post4 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post4, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post4 <- neutral_post4 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post4, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post4 <- phylo_post4 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post4, by = join_by(sample == Tube_code))

beta_func_nmds_post4 <- func_post4 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post4, by = join_by(sample == Tube_code))

6.3.4.3 Comparison between Acclimation vs Post-FMT1 and Post-FMT2

6.3.4.3.1 Number of samples used
[1] 79
6.3.4.3.2 Richness
richness_post6 <- as.matrix(beta_q0n$S)
richness_post6 <- as.dist(richness_post6[rownames(richness_post6) %in% samples_to_keep_post6,
               colnames(richness_post6) %in% samples_to_keep_post6])
betadisper(richness_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)   
Groups     1 0.09792 0.097923 18.607    999  0.002 **
Residuals 77 0.40523 0.005263                        
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
          Cold_wet Hot_dry
Cold_wet             0.001
Hot_dry  4.714e-05        
adonis2(richness_post6 ~ type*time_point,
        data = subset_meta_post6 %>% arrange(match(Tube_code,labels(richness_post6))),
        permutations = 999,
        strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(richness_post6))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 8 6.395467 0.2451322 2.841433 0.001
Residual 70 19.694403 0.7548678 NA NA
Total 78 26.089870 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_post6_arrange <- column_to_rownames(subset_meta_post6, "Tube_code")
subset_meta_post6_arrange<-subset_meta_post6_arrange[labels(richness_post6),]

pairwise <- pairwise.adonis(richness_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.3657243 1.123239 0.06966584 0.248 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.2605860 4.943717 0.24788342 0.001 0.036 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.6804988 2.100611 0.12283837 0.001 0.036 .
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8481258 2.673290 0.16033371 0.001 0.036 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.1180661 3.809275 0.20252111 0.001 0.036 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.8072604 2.940901 0.16392161 0.001 0.036 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.4817387 1.660775 0.09968176 0.026 0.936
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.1179704 3.885459 0.20573812 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.3606630 5.087152 0.24124415 0.001 0.036 .
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.7216200 2.172734 0.11956009 0.001 0.036 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9551308 2.926054 0.16322910 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.2263345 4.039487 0.20157637 0.001 0.036 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.9130048 3.195028 0.16645080 0.001 0.036 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.5959230 1.984036 0.11032208 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.2747787 4.275366 0.21086503 0.001 0.036 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.4319792 5.384836 0.25180628 0.002 0.072
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.8172413 3.194690 0.17558364 0.001 0.036 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.5796135 2.441615 0.13239702 0.001 0.036 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6397330 2.913695 0.15405213 0.001 0.036 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.4575447 6.224524 0.28007456 0.001 0.036 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3276169 1.412318 0.08111028 0.038 1.000
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.5615418 1.729004 0.10335366 0.010 0.360
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.8438429 2.793772 0.14865413 0.001 0.036 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.7628135 2.683925 0.14364890 0.002 0.072
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3432605 1.148733 0.06698647 0.236 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.1269580 3.799256 0.19188884 0.001 0.036 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.3734921 1.268929 0.07799710 0.122 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3571397 1.297184 0.07959561 0.139 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.7769467 2.670898 0.15114670 0.001 0.036 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.6502360 2.253407 0.13060650 0.002 0.072
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4132091 1.616138 0.09174188 0.013 0.468
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0163992 3.760571 0.19030682 0.001 0.036 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.2732563 1.019281 0.05988979 0.416 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.6463814 2.560441 0.13795154 0.001 0.036 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.4796256 1.916520 0.10696943 0.001 0.036 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.1305044 4.268317 0.21059061 0.001 0.036 .
6.3.4.3.3 Neutral
neutral_post6 <- as.matrix(beta_q1n$S)
neutral_post6 <- as.dist(neutral_post6[rownames(neutral_post6) %in% samples_to_keep_post6,
               colnames(neutral_post6) %in% samples_to_keep_post6])
betadisper(neutral_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)    
Groups     1 0.09524 0.095237 15.396    999  0.001 ***
Residuals 77 0.47632 0.006186                         
---
Signif. codes:  0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
           Cold_wet Hot_dry
Cold_wet              0.001
Hot_dry  0.00018818        
adonis2(neutral_post6 ~ type*time_point,
        data = subset_meta_post6 %>% arrange(match(Tube_code,labels(neutral_post6))),
        permutations = 999,
        strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(neutral_post6))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 8 7.736408 0.3121974 3.971673 0.001
Residual 70 17.044094 0.6878026 NA NA
Total 78 24.780501 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.2759858 0.9928976 0.06208366 0.460 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 1.4146464 6.5078325 0.30257965 0.001 0.036 .
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.8153894 3.0970603 0.17113610 0.001 0.036 .
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 1.1809241 4.4856470 0.24265567 0.001 0.036 .
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.4321524 5.7774260 0.27806264 0.001 0.036 .
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 1.1524353 4.9536068 0.24825621 0.001 0.036 .
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.4748999 1.9609749 0.11561688 0.006 0.216
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.3503168 5.4081420 0.26499923 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 1.6347704 6.8326887 0.29925029 0.002 0.072
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.9517634 3.3715700 0.17404733 0.001 0.036 .
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 1.3127773 4.6298256 0.23585668 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 1.6713369 6.2395460 0.28056085 0.001 0.036 .
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 1.3540292 5.3398081 0.25022756 0.001 0.036 .
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.6311089 2.4041625 0.13063146 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 1.6125755 5.9825981 0.27215155 0.001 0.036 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 1.5409781 6.8338056 0.29928456 0.001 0.036 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.9133614 4.0964534 0.21451383 0.001 0.036 .
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.6954835 3.2951234 0.17077493 0.001 0.036 .
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.6202327 3.1519868 0.16457754 0.001 0.036 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 1.5701179 7.6327037 0.32297209 0.001 0.036 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.3634438 1.7083388 0.09647087 0.035 1.000
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.6051778 2.2508491 0.13047758 0.013 0.468
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 1.0528902 4.1436369 0.20570451 0.001 0.036 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.8908158 3.7146920 0.18842252 0.001 0.036 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3860927 1.5521758 0.08843210 0.081 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.3122237 5.1302726 0.24279254 0.001 0.036 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.4150076 1.6372683 0.09840968 0.061 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3157079 1.3252026 0.08117526 0.157 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0579520 4.2700097 0.22158835 0.001 0.036 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.7454015 2.9200493 0.16294873 0.002 0.072
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4377161 1.9421261 0.10824392 0.003 0.108
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.3766597 5.8752789 0.26858075 0.001 0.036 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.3176516 1.3161367 0.07600637 0.184 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 1.0227481 4.6483346 0.22511910 0.001 0.036 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.5010202 2.2065321 0.12119453 0.002 0.072
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.3619424 5.7710313 0.26507845 0.001 0.036 .
6.3.4.3.4 Phylogenetic
phylo_post6 <- as.matrix(beta_q1p$S)
phylo_post6 <- as.dist(phylo_post6[rownames(phylo_post6) %in% samples_to_keep_post6,
               colnames(phylo_post6) %in% samples_to_keep_post6])
betadisper(phylo_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.02554 0.025541 2.1633    999  0.153
Residuals 77 0.90909 0.011806                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.153
Hot_dry   0.14542        
adonis2(phylo_post6 ~ type*time_point,
        data = subset_meta_post6 %>% arrange(match(Tube_code,labels(phylo_post6))),
        permutations = 999,
        strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(phylo_post6))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 8 1.055866 0.2692287 3.223651 0.001
Residual 70 2.865952 0.7307713 NA NA
Total 78 3.921818 1.0000000 NA NA
pairwise <- pairwise.adonis(phylo_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.05690611 0.7893300 0.04999136 0.506 1.000
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.10866997 3.0547314 0.16919285 0.012 0.432
Control.1_Acclimation vs Control.5_Post-FMT1 1 0.11760287 2.9988032 0.16661126 0.022 0.792
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.09624422 1.7247115 0.10968160 0.161 1.000
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.18586788 4.1904227 0.21836011 0.006 0.216
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.15591805 4.3792085 0.22597458 0.002 0.072
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.07099742 1.8793639 0.11134092 0.101 1.000
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.18682367 4.8787543 0.24542555 0.001 0.036 .
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.23108846 4.0521838 0.20208192 0.005 0.180
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.26358465 4.3608960 0.21417997 0.009 0.324
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.25319427 3.2738422 0.17915456 0.041 1.000
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.39050120 5.9837393 0.27218933 0.002 0.072
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 0.36496892 6.3966666 0.28560797 0.002 0.072
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.22628210 3.8292220 0.19311005 0.019 0.684
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.34830814 5.8463335 0.26761166 0.001 0.036 .
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 0.14203376 5.4200212 0.25303529 0.001 0.036 .
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.09666753 2.3682173 0.13635351 0.019 0.684
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.09252600 2.9824958 0.15711821 0.009 0.324
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.10002871 4.3836237 0.21505615 0.001 0.036 .
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 0.12577510 5.0601287 0.24027055 0.001 0.036 .
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.06334378 2.4997737 0.13512455 0.015 0.540
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.01842535 0.4144162 0.02688498 0.791 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.05987967 1.7387847 0.09802164 0.107 1.000
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07917244 3.0180046 0.15869197 0.010 0.360
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.04335491 1.5335604 0.08746429 0.179 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.10783045 3.7500438 0.18987521 0.001 0.036 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.03212966 0.6477782 0.04139746 0.707 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.06393539 1.5651817 0.09448624 0.147 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.05265949 1.2240203 0.07544494 0.293 1.000
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.09753501 2.2402429 0.12994265 0.012 0.432
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07228545 2.3279593 0.12701683 0.036 1.000
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.11759094 3.5538444 0.18174658 0.001 0.036 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.06667255 1.9859527 0.11041687 0.108 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.05927454 2.3820253 0.12958449 0.026 0.936
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.06906280 2.7224602 0.14541146 0.005 0.180
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.11081709 4.0436561 0.20174244 0.002 0.072
6.3.4.3.5 Functional
func_post6 <- as.matrix(beta_q1f$S)
func_post6 <- as.dist(func_post6[rownames(func_post6) %in% samples_to_keep_post6,
               colnames(func_post6) %in% samples_to_keep_post6])
betadisper(func_post6, subset_meta_post6$Population) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq  Mean Sq      F N.Perm Pr(>F)
Groups     1 0.00018 0.000175 0.0093    999  0.926
Residuals 77 1.45162 0.018852                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
         Cold_wet Hot_dry
Cold_wet            0.921
Hot_dry   0.92349        
adonis2(func_post6 ~ type*time_point,
        data = subset_meta_post6 %>% arrange(match(Tube_code,labels(func_post6))),
        permutations = 999,
        strata = subset_meta_post6 %>% arrange(match(Tube_code,labels(func_post6))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 8 0.1632318 0.07638509 0.7236452 0.56
Residual 70 1.9737271 0.92361491 NA NA
Total 78 2.1369589 1.00000000 NA NA
pairwise <- pairwise.adonis(func_post6, subset_meta_post6_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.1_Acclimation vs Treatment.1_Acclimation 1 0.0220766636 0.650021923 0.0415348890 0.400 1
Control.1_Acclimation vs Hot_control.1_Acclimation 1 0.0167496054 0.520911459 0.0335619117 0.448 1
Control.1_Acclimation vs Control.5_Post-FMT1 1 -0.0083255553 -0.228001097 -0.0154346814 0.888 1
Control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.1265279179 3.455505193 0.1979607668 0.082 1
Control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.0510084290 1.239518381 0.0763272870 0.286 1
Control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.0376809680 1.119650309 0.0694587220 0.305 1
Control.1_Acclimation vs Control.6_Post-FMT2 1 0.0292200956 0.920511083 0.0578191917 0.379 1
Control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0133433458 0.270473784 0.0177122064 0.507 1
Treatment.1_Acclimation vs Hot_control.1_Acclimation 1 0.0010225901 0.054303886 0.0033825127 0.651 1
Treatment.1_Acclimation vs Control.5_Post-FMT1 1 0.0021570675 0.094115687 0.0058478321 0.635 1
Treatment.1_Acclimation vs Treatment.5_Post-FMT1 1 0.0566023632 2.560370692 0.1458039091 0.185 1
Treatment.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.0095691236 0.350955208 0.0214638964 0.495 1
Treatment.1_Acclimation vs Treatment.6_Post-FMT2 1 -0.0005177706 -0.025585400 -0.0016016487 0.750 1
Treatment.1_Acclimation vs Control.6_Post-FMT2 1 0.0013301207 0.072110871 0.0044867082 0.606 1
Treatment.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0060959077 0.174487757 0.0107878382 0.576 1
Hot_control.1_Acclimation vs Control.5_Post-FMT1 1 -0.0017456629 -0.082250179 -0.0051671989 0.703 1
Hot_control.1_Acclimation vs Treatment.5_Post-FMT1 1 0.0577586745 2.845456220 0.1594499006 0.172 1
Hot_control.1_Acclimation vs Hot_control.5_Post-FMT1 1 0.0055752661 0.218035597 0.0134440201 0.530 1
Hot_control.1_Acclimation vs Treatment.6_Post-FMT2 1 0.0010345754 0.055797964 0.0034752533 0.671 1
Hot_control.1_Acclimation vs Control.6_Post-FMT2 1 -0.0001056284 -0.006306177 -0.0003942915 0.693 1
Hot_control.1_Acclimation vs Hot_control.6_Post-FMT2 1 0.0017235602 0.051851181 0.0032302306 0.746 1
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.1195408549 4.847647043 0.2442429086 0.063 1
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0525878365 1.773089316 0.0997625840 0.206 1
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0265995825 1.175418056 0.0684360667 0.335 1
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0145818992 0.699759916 0.0419023938 0.420 1
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0080695208 -0.216173226 -0.0136958691 0.916 1
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0129803540 0.443076619 0.0286909552 0.460 1
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0267162134 1.225605811 0.0755352882 0.311 1
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0384388433 1.932815819 0.1141461550 0.220 1
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.0553988290 1.478193905 0.0897060633 0.258 1
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 -0.0040061386 -0.148504693 -0.0093684974 0.726 1
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0024023972 0.095389804 0.0059265296 0.612 1
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0004960759 -0.011903277 -0.0007445087 0.855 1
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 -0.0080428882 -0.442986255 -0.0284750185 0.859 1
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 -0.0011796256 -0.034047378 -0.0021324990 0.897 1
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.0036300838 0.110487573 0.0068581148 0.707 1
beta_richness_nmds_post6 <- richness_post6 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post6, by = c("sample" = "Tube_code"))

beta_neutral_nmds_post6 <- neutral_post6 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post6, by = c("sample" = "Tube_code"))

beta_phylo_nmds_post6 <- phylo_post6 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post6, by = join_by(sample == Tube_code))

beta_func_nmds_post6 <- func_post6 %>%
  metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
  scores() %>%
  as_tibble(., rownames = "sample") %>%
  left_join(subset_meta_post6, by = join_by(sample == Tube_code))

6.3.5 5. Are there differences between the control and the treatment group?

6.3.5.1 After 1 week –> Post-FMT1

6.3.5.1.1 Number of samples used
[1] 26
6.3.5.1.2 Richness
richness_post1 <- as.matrix(beta_q0n$S)
richness_post1 <- as.dist(richness_post1[rownames(richness_post1) %in% samples_to_keep_post1,
               colnames(richness_post1) %in% samples_to_keep_post1])
betadisper(richness_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.017675 0.0088373 2.3825    999  0.105
Residuals 23 0.085312 0.0037092                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
              Control Hot_control Treatment
Control                 0.0040000     0.668
Hot_control 0.0068795                 0.213
Treatment   0.6248469   0.2084296          
adonis2(richness_post1 ~ type,
        data = subset_meta_post1 %>% arrange(match(Tube_code,labels(richness_post1))),
        permutations = 999,
        strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(richness_post1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 1.195567 0.1448246 1.947534 1
Residual 23 7.059710 0.8551754 NA NA
Total 25 8.255277 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_post1_arrange <- column_to_rownames(subset_meta_post1, "Tube_code")
subset_meta_post1_arrange<-subset_meta_post1_arrange[labels(richness_post1),]

pairwise <- pairwise.adonis(richness_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control vs Treatment 1 0.5615418 1.729004 0.1033537 0.020 0.060
Control vs Hot_control 1 0.8438429 2.793772 0.1486541 0.001 0.003 *
Treatment vs Hot_control 1 0.3734921 1.268929 0.0779971 0.129 0.387
6.3.5.1.3 Neutral
neutral_post1 <- as.matrix(beta_q1n$S)
neutral_post1 <- as.dist(neutral_post1[rownames(neutral_post1) %in% samples_to_keep_post1,
               colnames(neutral_post1) %in% samples_to_keep_post1])
betadisper(neutral_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.011001 0.0055005 0.6303    999  0.572
Residuals 23 0.200714 0.0087267                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.22500     0.955
Hot_control 0.21166                 0.446
Treatment   0.95468     0.43604          
adonis2(neutral_post1 ~ type,
        data = subset_meta_post1 %>% arrange(match(Tube_code,labels(neutral_post1))),
        permutations = 999,
        strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(neutral_post1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 1.395968 0.1900228 2.697931 1
Residual 23 5.950350 0.8099772 NA NA
Total 25 7.346318 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control vs Treatment 1 0.6051778 2.250849 0.13047758 0.013 0.039 .
Control vs Hot_control 1 1.0528902 4.143637 0.20570451 0.002 0.006 *
Treatment vs Hot_control 1 0.4150076 1.637268 0.09840968 0.050 0.150
6.3.5.1.4 Phylogenetic
phylo_post1 <- as.matrix(beta_q1p$S)
phylo_post1 <- as.dist(phylo_post1[rownames(phylo_post1) %in% samples_to_keep_post1,
               colnames(phylo_post1) %in% samples_to_keep_post1])
betadisper(phylo_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00440 0.0021994 0.1369    999  0.908
Residuals 23 0.36941 0.0160614                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.93200     0.698
Hot_control 0.91505                 0.776
Treatment   0.63312     0.73046          
adonis2(phylo_post1 ~ type,
        data = subset_meta_post1 %>% arrange(match(Tube_code,labels(phylo_post1))),
        permutations = 999,
        strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(phylo_post1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 0.07451036 0.07059466 0.8735033 1
Residual 23 0.98095695 0.92940534 NA NA
Total 25 1.05546731 1.00000000 NA NA
pairwise <- pairwise.adonis(phylo_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control vs Treatment 1 0.01842535 0.4144162 0.02688498 0.791 1.000
Control vs Hot_control 1 0.05987967 1.7387847 0.09802164 0.122 0.366
Treatment vs Hot_control 1 0.03212966 0.6477782 0.04139746 0.712 1.000
6.3.5.1.5 Functional
func_post1 <- as.matrix(beta_q1f$S)
func_post1 <- as.dist(func_post1[rownames(func_post1) %in% samples_to_keep_post1,
               colnames(func_post1) %in% samples_to_keep_post1])
betadisper(func_post1, subset_meta_post1$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00400 0.0019999 0.1431    999   0.88
Residuals 23 0.32135 0.0139717                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.61100     0.766
Hot_control 0.60188                 0.845
Treatment   0.74597     0.84473          
adonis2(func_post1 ~ type,
        data = subset_meta_post1 %>% arrange(match(Tube_code,labels(func_post1))),
        permutations = 999,
        strata = subset_meta_post1 %>% arrange(match(Tube_code,labels(func_post1))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 0.1230554 0.1608583 2.204479 1
Residual 23 0.6419374 0.8391417 NA NA
Total 25 0.7649929 1.0000000 NA NA
pairwise <- pairwise.adonis(func_post1, subset_meta_post1_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control vs Treatment 1 0.11954085 4.8476470 0.24424291 0.086 0.258
Control vs Hot_control 1 0.05258784 1.7730893 0.09976258 0.245 0.735
Treatment vs Hot_control 1 0.01298035 0.4430766 0.02869096 0.452 1.000
beta_richness_nmds_post1 <- richness_post1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post1, by = join_by(sample == Tube_code))

beta_neutral_nmds_post1 <- neutral_post1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post1, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post1 <- phylo_post1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post1, by = join_by(sample == Tube_code))

beta_functional_nmds_post1 <- func_post1 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post1, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post1 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")

6.3.5.2 After 2 weeks –>Post-FMT2

post2 <- meta %>%
  filter(time_point == "6_Post-FMT2")

post2.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post2))]
identical(sort(colnames(post2.counts)),sort(as.character(rownames(post2))))

post2_nmds <- sample_metadata %>%
  filter(time_point == "6_Post-FMT2")
6.3.5.2.1 Number of samples used
[1] 27
6.3.5.2.2 Richness
richness_post2 <- as.matrix(beta_q0n$S)
richness_post2 <- as.dist(richness_post2[rownames(richness_post2) %in% samples_to_keep_post2,
               colnames(richness_post2) %in% samples_to_keep_post2])
betadisper(richness_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.002011 0.0010056 0.1982    999  0.826
Residuals 24 0.121775 0.0050740                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.70400     0.801
Hot_control 0.67789                 0.645
Treatment   0.79246     0.59820          
adonis2(richness_post2 ~ type,
        data = subset_meta_post2 %>% arrange(match(Tube_code,labels(richness_post2))),
        permutations = 999,
        strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(richness_post2))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 1.504341 0.1967776 2.939822 1
Residual 24 6.140538 0.8032224 NA NA
Total 26 7.644879 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_post2_arrange <- column_to_rownames(subset_meta_post2, "Tube_code")
subset_meta_post2_arrange<-subset_meta_post2_arrange[labels(richness_post2),]

pairwise <- pairwise.adonis(richness_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 0.6463814 2.560441 0.1379515 0.002 0.006 *
Treatment vs Hot_control 1 0.4796256 1.916520 0.1069694 0.001 0.003 *
Control vs Hot_control 1 1.1305044 4.268317 0.2105906 0.001 0.003 *
6.3.5.2.3 Neutral
neutral_post2 <- as.matrix(beta_q1n$S)
neutral_post2 <- as.dist(neutral_post2[rownames(neutral_post2) %in% samples_to_keep_post2,
               colnames(neutral_post2) %in% samples_to_keep_post2])
betadisper(neutral_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.008262 0.0041311 0.8024    999  0.443
Residuals 24 0.123559 0.0051483                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.44400     0.676
Hot_control 0.44675                 0.238
Treatment   0.65989     0.25095          
adonis2(neutral_post2 ~ type,
        data = subset_meta_post2 %>% arrange(match(Tube_code,labels(neutral_post2))),
        permutations = 999,
        strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(neutral_post2))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 1.923807 0.2603795 4.224537 1
Residual 24 5.464666 0.7396205 NA NA
Total 26 7.388473 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 1.0227481 4.648335 0.2251191 0.001 0.003 *
Treatment vs Hot_control 1 0.5010202 2.206532 0.1211945 0.002 0.006 *
Control vs Hot_control 1 1.3619424 5.771031 0.2650785 0.001 0.003 *
6.3.5.2.4 Phylogenetic
phylo_post2 <- as.matrix(beta_q1p$S)
phylo_post2 <- as.dist(phylo_post2[rownames(phylo_post2) %in% samples_to_keep_post2,
               colnames(phylo_post2) %in% samples_to_keep_post2])
betadisper(phylo_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df   Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.000407 0.0002034 0.0487    999  0.961
Residuals 24 0.100305 0.0041794                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.93700     0.852
Hot_control 0.93765                 0.757
Treatment   0.83933     0.76015          
adonis2(phylo_post2 ~ type,
        data = subset_meta_post2 %>% arrange(match(Tube_code,labels(phylo_post2))),
        permutations = 999,
        strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(phylo_post2))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 0.1594363 0.2042241 3.079623 1
Residual 24 0.6212564 0.7957759 NA NA
Total 26 0.7806927 1.0000000 NA NA
pairwise <- pairwise.adonis(phylo_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 0.05927454 2.382025 0.1295845 0.032 0.096
Treatment vs Hot_control 1 0.06906280 2.722460 0.1454115 0.005 0.015 .
Control vs Hot_control 1 0.11081709 4.043656 0.2017424 0.001 0.003 *
6.3.5.2.5 Functional
func_post2 <- as.matrix(beta_q1f$S)
func_post2 <- as.dist(func_post2[rownames(func_post2) %in% samples_to_keep_post2,
               colnames(func_post2) %in% samples_to_keep_post2])
betadisper(func_post2, subset_meta_post2$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.01259 0.0062962 0.3249    999   0.79
Residuals 24 0.46507 0.0193778                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.53000     0.638
Hot_control 0.45381                 0.774
Treatment   0.57452     0.74365          
adonis2(func_post2 ~ type,
        data = subset_meta_post2 %>% arrange(match(Tube_code,labels(func_post2))),
        permutations = 999,
        strata = subset_meta_post2 %>% arrange(match(Tube_code,labels(func_post2))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 2 -0.003728287 -0.005470434 -0.06528805 1
Residual 24 0.685262325 1.005470434 NA NA
Total 26 0.681534039 1.000000000 NA NA
pairwise <- pairwise.adonis(func_post2, subset_meta_post2_arrange$type, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Treatment vs Control 1 -0.008042888 -0.44298625 -0.028475019 0.829 1
Treatment vs Hot_control 1 -0.001179626 -0.03404738 -0.002132499 0.890 1
Control vs Hot_control 1 0.003630084 0.11048757 0.006858115 0.693 1
beta_richness_nmds_post2 <- richness_post2 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post2, by = join_by(sample == Tube_code))

beta_neutral_nmds_post2 <- neutral_post2 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post2, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post2 <- phylo_post2 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post2, by = join_by(sample == Tube_code))

beta_functional_nmds_post2 <- func_post2 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post2, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post2 %>%
            group_by(type) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")

6.3.5.3 Post1 vs Post2

post5 <- meta %>%
  filter(time_point == "6_Post-FMT2" | time_point == "5_Post-FMT1")

post5.counts <- temp_genome_counts[,which(colnames(temp_genome_counts) %in% rownames(post5))]
identical(sort(colnames(post5.counts)),sort(as.character(rownames(post5))))

post5_nmds <- sample_metadata %>%
  filter(time_point == "6_Post-FMT2"| time_point == "5_Post-FMT1")
6.3.5.3.1 Number of samples used
[1] 53
6.3.5.3.2 Richness
richness_post5 <- as.matrix(beta_q0n$S)
richness_post5 <- as.dist(richness_post5[rownames(richness_post5) %in% samples_to_keep_post5,
               colnames(richness_post5) %in% samples_to_keep_post5])
betadisper(richness_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.01841 0.0092048 1.7364    999   0.19
Residuals 50 0.26505 0.0053010                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
             Control Hot_control Treatment
Control                 0.037000     0.712
Hot_control 0.039117                 0.227
Treatment   0.716358    0.218648          
adonis2(richness_post5 ~ type*time_point,
        data = subset_meta_post5 %>% arrange(match(Tube_code,labels(richness_post5))),
        permutations = 999,
        strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(richness_post5))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 5 3.261916 0.1981463 2.322836 0.001
Residual 47 13.200248 0.8018537 NA NA
Total 52 16.462164 1.0000000 NA NA
#Arrange of metadata dataframe
subset_meta_post5_arrange <- column_to_rownames(subset_meta_post5, "Tube_code")
subset_meta_post5_arrange<-subset_meta_post5_arrange[labels(richness_post5),]

pairwise <- pairwise.adonis(richness_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.5615418 1.729004 0.10335366 0.015 0.225
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.8438429 2.793772 0.14865413 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.7628135 2.683925 0.14364890 0.001 0.015 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3432605 1.148733 0.06698647 0.264 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.1269580 3.799256 0.19188884 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.3734921 1.268929 0.07799710 0.097 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3571397 1.297184 0.07959561 0.137 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.7769467 2.670898 0.15114670 0.001 0.015 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.6502360 2.253407 0.13060650 0.002 0.030 .
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4132091 1.616138 0.09174188 0.012 0.180
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0163992 3.760571 0.19030682 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.2732563 1.019281 0.05988979 0.433 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.6463814 2.560441 0.13795154 0.001 0.015 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.4796256 1.916520 0.10696943 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.1305044 4.268317 0.21059061 0.001 0.015 .
6.3.5.3.3 Neutral
neutral_post5 <- as.matrix(beta_q1n$S)
neutral_post5 <- as.dist(neutral_post5[rownames(neutral_post5) %in% samples_to_keep_post5,
               colnames(neutral_post5) %in% samples_to_keep_post5])
betadisper(neutral_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq     F N.Perm Pr(>F)
Groups     2 0.01992 0.0099587 1.565    999  0.212
Residuals 50 0.31818 0.0063636                    

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.11400     0.876
Hot_control 0.10701                 0.165
Treatment   0.87156     0.17449          
adonis2(neutral_post5 ~ type*time_point,
        data = subset_meta_post5 %>% arrange(match(Tube_code,labels(neutral_post5))),
        permutations = 999,
        strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(neutral_post5))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 5 3.947979 0.2569798 3.251069 0.001
Residual 47 11.415016 0.7430202 NA NA
Total 52 15.362995 1.0000000 NA NA
pairwise <- pairwise.adonis(neutral_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.6051778 2.250849 0.13047758 0.019 0.285
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 1.0528902 4.143637 0.20570451 0.001 0.015 .
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.8908158 3.714692 0.18842252 0.002 0.030 .
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.3860927 1.552176 0.08843210 0.094 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 1.3122237 5.130273 0.24279254 0.002 0.030 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.4150076 1.637268 0.09840968 0.056 0.840
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.3157079 1.325203 0.08117526 0.152 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.0579520 4.270010 0.22158835 0.002 0.030 .
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.7454015 2.920049 0.16294873 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.4377161 1.942126 0.10824392 0.006 0.090
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 1.3766597 5.875279 0.26858075 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.3176516 1.316137 0.07600637 0.199 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 1.0227481 4.648335 0.22511910 0.002 0.030 .
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.5010202 2.206532 0.12119453 0.001 0.015 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 1.3619424 5.771031 0.26507845 0.001 0.015 .
6.3.5.3.4 Phylogenetic
phylo_post5 <- as.matrix(beta_q1p$S)
phylo_post5 <- as.dist(phylo_post5[rownames(phylo_post5) %in% samples_to_keep_post5,
               colnames(phylo_post5) %in% samples_to_keep_post5])
betadisper(phylo_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00051 0.0002543 0.0265    999  0.973
Residuals 50 0.47996 0.0095993                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.88500     0.837
Hot_control 0.88926                 0.926
Treatment   0.82391     0.91902          
adonis2(phylo_post5 ~ type*time_point,
        data = subset_meta_post5 %>% arrange(match(Tube_code,labels(phylo_post5))),
        permutations = 999,
        strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(phylo_post5))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 5 0.3518222 0.180049 2.0641 0.001
Residual 47 1.6022134 0.819951 NA NA
Total 52 1.9540356 1.000000 NA NA
pairwise <- pairwise.adonis(phylo_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.01842535 0.4144162 0.02688498 0.768 1.000
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.05987967 1.7387847 0.09802164 0.134 1.000
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07917244 3.0180046 0.15869197 0.010 0.150
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.04335491 1.5335604 0.08746429 0.196 1.000
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.10783045 3.7500438 0.18987521 0.002 0.030 .
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.03212966 0.6477782 0.04139746 0.689 1.000
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.06393539 1.5651817 0.09448624 0.131 1.000
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.05265949 1.2240203 0.07544494 0.295 1.000
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.09753501 2.2402429 0.12994265 0.016 0.240
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.07228545 2.3279593 0.12701683 0.031 0.465
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.11759094 3.5538444 0.18174658 0.001 0.015 .
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.06667255 1.9859527 0.11041687 0.097 1.000
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 0.05927454 2.3820253 0.12958449 0.025 0.375
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.06906280 2.7224602 0.14541146 0.003 0.045 .
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.11081709 4.0436561 0.20174244 0.002 0.030 .
6.3.5.3.5 Functional
func_post5 <- as.matrix(beta_q1f$S)
func_post5 <- as.dist(func_post5[rownames(func_post5) %in% samples_to_keep_post5,
               colnames(func_post5) %in% samples_to_keep_post5])
betadisper(func_post5, subset_meta_post5$type) %>% permutest(., pairwise = TRUE)

Permutation test for homogeneity of multivariate dispersions
Permutation: free
Number of permutations: 999

Response: Distances
          Df  Sum Sq   Mean Sq      F N.Perm Pr(>F)
Groups     2 0.00785 0.0039232 0.2322    999  0.818
Residuals 50 0.84483 0.0168966                     

Pairwise comparisons:
(Observed p-value below diagonal, permuted p-value above diagonal)
            Control Hot_control Treatment
Control                 0.57000     0.599
Hot_control 0.52384                 0.860
Treatment   0.58787     0.85068          
adonis2(func_post5 ~ type*time_point,
        data = subset_meta_post5 %>% arrange(match(Tube_code,labels(func_post5))),
        permutations = 999,
        strata = subset_meta_post5 %>% arrange(match(Tube_code,labels(func_post5))) %>% pull(individual)) %>%
        broom::tidy() %>%
        tt()
term df SumOfSqs R2 statistic p.value
Model 5 0.1076682 0.07503698 0.7625685 0.499
Residual 47 1.3271997 0.92496302 NA NA
Total 52 1.4348679 1.00000000 NA NA
pairwise <- pairwise.adonis(func_post5, subset_meta_post5_arrange$type_time, perm=999)
pairwise%>%
  tt()
pairs Df SumsOfSqs F.Model R2 p.value p.adjusted sig
Control.5_Post-FMT1 vs Treatment.5_Post-FMT1 1 0.1195408549 4.84764704 0.2442429086 0.072 1
Control.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0525878365 1.77308932 0.0997625840 0.218 1
Control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0265995825 1.17541806 0.0684360667 0.303 1
Control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0145818992 0.69975992 0.0419023938 0.437 1
Control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0080695208 -0.21617323 -0.0136958691 0.921 1
Treatment.5_Post-FMT1 vs Hot_control.5_Post-FMT1 1 0.0129803540 0.44307662 0.0286909552 0.480 1
Treatment.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 0.0267162134 1.22560581 0.0755352882 0.338 1
Treatment.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0384388433 1.93281582 0.1141461550 0.239 1
Treatment.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 0.0553988290 1.47819391 0.0897060633 0.260 1
Hot_control.5_Post-FMT1 vs Treatment.6_Post-FMT2 1 -0.0040061386 -0.14850469 -0.0093684974 0.735 1
Hot_control.5_Post-FMT1 vs Control.6_Post-FMT2 1 0.0024023972 0.09538980 0.0059265296 0.604 1
Hot_control.5_Post-FMT1 vs Hot_control.6_Post-FMT2 1 -0.0004960759 -0.01190328 -0.0007445087 0.849 1
Treatment.6_Post-FMT2 vs Control.6_Post-FMT2 1 -0.0080428882 -0.44298625 -0.0284750185 0.832 1
Treatment.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 -0.0011796256 -0.03404738 -0.0021324990 0.913 1
Control.6_Post-FMT2 vs Hot_control.6_Post-FMT2 1 0.0036300838 0.11048757 0.0068581148 0.703 1
beta_richness_nmds_post5 <- richness_post5 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post5, by = join_by(sample == Tube_code))

beta_neutral_nmds_post5 <- neutral_post5 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post5, by = join_by(sample == Tube_code))

beta_phylogenetic_nmds_post5 <- phylo_post5 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post5, by = join_by(sample == Tube_code))

beta_functional_nmds_post5 <- func_post5 %>%
                metaMDS(.,trymax = 500, k=2, verbosity=FALSE) %>%
                scores() %>%
                as_tibble(., rownames = "sample") %>%
                left_join(subset_meta_post5, by = join_by(sample == Tube_code))
p0<-beta_richness_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Richness beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p1<-beta_neutral_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
        scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y = "NMDS2", x="NMDS1 \n Neutral beta diversity") +
                theme_classic() +
                theme(legend.position="none")
  
p2<-beta_phylogenetic_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Phylogenetic beta diversity") +
                theme_classic() +
                theme(legend.position="none")

p3<-beta_functional_nmds_post5 %>%
            group_by(type, time_point) %>%
            mutate(x_cen = mean(NMDS1, na.rm = TRUE)) %>%
            mutate(y_cen = mean(NMDS2, na.rm = TRUE)) %>%
            ungroup() %>%
            ggplot(., aes(x=NMDS1,y=NMDS2, color=type, shape=time_point)) +
                scale_color_manual(name="Type",
                       breaks=c("Control", "Hot_control", "Treatment"),
                       labels=c("Cold-Cold", "Hot-Hot", "Cold-Hot"),
                       values=c("#4477AA","#d57d2c","#76b183")) +
                geom_point(size=2) +
                geom_segment(aes(x=x_cen, y=y_cen, xend=NMDS1, yend=NMDS2), alpha=0.2) +
        labs(y= element_blank (), x="NMDS1 \n Functional beta diversity") +
                theme_classic()+
                theme(legend.position="none")
ggarrange(p0, p1, p2, p3, ncol=2, nrow=2, common.legend = TRUE, legend="right")